Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients
dc.contributor.author | Díaz Ramón, José Luis | |
dc.contributor.author | Gardeazabal García, Jesús | |
dc.contributor.author | Izu Belloso, Rosa María | |
dc.contributor.author | Garrote Contreras, Estíbaliz | |
dc.contributor.author | Rasero, Javier | |
dc.contributor.author | Apraiz García, Aintzane | |
dc.contributor.author | Penas Lago, Cristina | |
dc.contributor.author | Seijo Fernández, Sandra | |
dc.contributor.author | López Saratxaga, Cristina | |
dc.contributor.author | De La Peña, Pedro María | |
dc.contributor.author | Sánchez Díez, Ana | |
dc.contributor.author | Cancho Galán, Goikoane | |
dc.contributor.author | Velasco, Verónica | |
dc.contributor.author | Sevilla Mambrilla, Arrate | |
dc.contributor.author | Fernández, David | |
dc.contributor.author | Cuenca, Iciar | |
dc.contributor.author | Cortés Díaz, Jesús María | |
dc.contributor.author | Alonso Alegre, Santos | |
dc.contributor.author | Asumendi Mallea, Aintzane | |
dc.contributor.author | Boyano López, María Dolores | |
dc.date.accessioned | 2023-04-27T12:19:27Z | |
dc.date.available | 2023-04-27T12:19:27Z | |
dc.date.issued | 2023-04-06 | |
dc.identifier.citation | Cancers 15(7) : (2023) // Article ID 2174 | es_ES |
dc.identifier.issn | 2072-6694 | |
dc.identifier.uri | http://hdl.handle.net/10810/60954 | |
dc.description.abstract | This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies. An AI algorithm was used to improve the early diagnosis of melanoma and to predict the risk of metastasis and of disease-free survival. Two models were obtained to predict metastasis (including “all patients” or only patients “at early stages of melanoma”), and a series of attributes were seen to predict the progression of metastasis: Breslow thickness, infiltrating BCL-2 expressing lymphocytes, and IL-4 and IL-6 serum levels. Importantly, a decrease in serum GM-CSF seems to be a marker of poor prognosis in patients with early-stage melanomas. | es_ES |
dc.description.sponsorship | This project was supported by grants to M.D.B. from the Basque Government (KK2017-041 and KK2020-00069); UPV/EHU (GIU17/066); H2020-ESCEL JTI (15/01); and MINECO (PCIN-2015-241) and to SA from Basque Government (IT693-22). CP holds a predoctoral fellowship from the Basque Government. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | melanoma | es_ES |
dc.subject | biomarkers | es_ES |
dc.subject | diagnosis | es_ES |
dc.subject | prognosis | es_ES |
dc.subject | machine learning | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | artificial intelligence | es_ES |
dc.subject | metastasis | es_ES |
dc.subject | disease-free | es_ES |
dc.subject | risk factors | es_ES |
dc.title | Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2023-04-12T13:24:20Z | |
dc.rights.holder | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2072-6694/15/7/2174 | es_ES |
dc.identifier.doi | 10.3390/cancers15072174 | |
dc.departamentoes | Biología celular e histología | |
dc.departamentoes | Dermatología, oftalmología y otorrinolaringología | |
dc.departamentoes | Genética, antropología física y fisiología animal | |
dc.departamentoeu | Zelulen biologia eta histologia | |
dc.departamentoeu | Dermatologia, oftalmologia eta otorrinolaringologia | |
dc.departamentoeu | Genetika,antropologia fisikoa eta animalien fisiologia |
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Except where otherwise noted, this item's license is described as © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).